code: def infer(context, top_k=40, top_p=0.9, temp=1.0, gen_len=512):
      tokens = tokenizer.encode(context)
  
      provided_ctx = len(tokens)
      pad_amount = seq - provided_ctx
  
      padded_tokens = np.pad(tokens, ((pad_amount, 0),)).astype(
          np.uint32
      )
      batched_tokens = np.array([padded_tokens] * per_replica_batch)
      length = np.ones(per_replica_batch, dtype=np.uint32) * len(tokens)
  
      start = time.time()
      output = network.generate(
          batched_tokens,
          length,
          gen_len,
          {
              "top_p": np.ones(per_replica_batch) * top_p,
              "top_k": top_k is not None
              and (np.ones(per_replica_batch, dtype=np.int32) * top_k)
              or None,
              "temp": np.ones(per_replica_batch) * temp,
          },
      )
  
      samples = []
      decoded_tokens = output[1][0]
  
      for o in decoded_tokens[:, :, 0]:
          samples.append(tokenizer.decode(o))
  
      print(f"completion done in {time.time() - start:06}s")
      return samples
  
file_path: mesh-transformer-jax/resharding_example.py
imports: import time
  import jax
  from jax.experimental import maps
  import numpy
  import optax
  import transformers
  from mesh_transformer.checkpoint import read_ckpt
  from mesh_transformer.sampling import nucleaus_sample
  from mesh_transformer.transformer_shard import CausalTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: infer(context, top_k, top_p, temp, gen_len) and returns: samples